class_docstr: Class for transition based parser. Implement 2 algorithms which are "arc-standard" and "arc-eager"
docstr: Create the training example in the libsvm format and write it to the input_file.
  Reference : 'A Dynamic Oracle for Arc-Eager Dependency Parsing' by Joav Goldberg and Joakim Nivre
docstr_tok: ["Create","the","training","example","in","the","libsvm","format","and","write","it","to","the","input_file",".","Reference",":","'","A","Dynamic","Oracle","for","Arc-Eager","Dependency","Parsing","'","by","Joav","Goldberg","and","Joakim","Nivre"]
code: def _create_training_examples_arc_eager(self, depgraphs, input_file):
      """
      Create the training example in the libsvm format and write it to the input_file.
      Reference : 'A Dynamic Oracle for Arc-Eager Dependency Parsing' by Joav Goldberg and Joakim Nivre
      """
      operation = Transition(self.ARC_EAGER)
      countProj = 0
      training_seq = []
  
      for depgraph in depgraphs:
          if not self._is_projective(depgraph):
              continue
  
          countProj += 1
          conf = Configuration(depgraph)
          while len(conf.buffer) > 0:
              b0 = conf.buffer[0]
              features = conf.extract_features()
              binary_features = self._convert_to_binary_features(
                  features
              )
  
              if len(conf.stack) > 0:
                  s0 = conf.stack[len(conf.stack) - 1]
                  # Left-arc operation
                  rel = self._get_dep_relation(b0, s0, depgraph)
                  if rel is not None:
                      key = Transition.LEFT_ARC + ":" + rel
                      self._write_to_file(
                          key, binary_features, input_file
                      )
                      operation.left_arc(conf, rel)
                      training_seq.append(key)
                      continue
  
                  # Right-arc operation
                  rel = self._get_dep_relation(s0, b0, depgraph)
                  if rel is not None:
                      key = Transition.RIGHT_ARC + ":" + rel
                      self._write_to_file(
                          key, binary_features, input_file
                      )
                      operation.right_arc(conf, rel)
                      training_seq.append(key)
                      continue
  
                  # reduce operation
                  flag = False
                  for k in range(s0):
                      if (
                          self._get_dep_relation(k, b0, depgraph)
                          is not None
                      ):
                          flag = True
                      if (
                          self._get_dep_relation(b0, k, depgraph)
                          is not None
                      ):
                          flag = True
                  if flag:
                      key = Transition.REDUCE
                      self._write_to_file(
                          key, binary_features, input_file
                      )
                      operation.reduce(conf)
                      training_seq.append(key)
                      continue
  
              # Shift operation as the default
              key = Transition.SHIFT
              self._write_to_file(key, binary_features, input_file)
              operation.shift(conf)
              training_seq.append(key)
  
      print(" Number of training examples : " + str(len(depgraphs)))
      print(
          " Number of valid (projective) examples : " + str(countProj)
      )
      return training_seq
  
imports: import pickle
  import tempfile
  from copy import deepcopy
  from operator import itemgetter
  from os import remove
  from nltk.parse import DependencyEvaluator, DependencyGraph, ParserI
label_desc: Use image node_id 7 for calling the TransitionParser obj's underlying member method code with example usage: obj._create_training_examples_arc_eager(depgraphs, input_file) and returns: training_seq